Zhenliang Ma

Orcid: 0000-0002-2141-0389

According to our database1, Zhenliang Ma authored at least 22 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Traffic Signal Phase and Timing Estimation Using Trajectory Data From Radar Vision Integrated Camera.
IEEE Trans. Intell. Transp. Syst., November, 2024

A Reverse Auction-Based Individualized Incentive System for Transit Mobility Management.
IEEE Trans. Intell. Transp. Syst., November, 2024

PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using Spatial-Temporal Attention Graph Convolution Networks.
IEEE Trans. Intell. Transp. Syst., October, 2024

SA-BiGCN: Bi-Stream Graph Convolution Networks With Spatial Attentions for the Eye Contact Detection in the Wild.
IEEE Trans. Intell. Transp. Syst., February, 2024

MuGIL: A Multi-Graph Interaction Learning Network for Multi-Task Traffic Prediction.
Knowl. Based Syst., 2024

2023
An integrated ride-matching and vehicle-rebalancing model for shared mobility on-demand services.
Comput. Oper. Res., November, 2023

User-station attention inference using smart card data: a knowledge graph assisted matrix decomposition model.
Appl. Intell., October, 2023

DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times.
IEEE Trans. Intell. Transp. Syst., June, 2023

Ex Post Path Choice Estimation for Urban Rail Systems Using Smart Card Data: An Aggregated Time-Space Hypernetwork Approach.
Transp. Sci., March, 2023

An Unsupervised Learning Approach for Robust Denied Boarding Probability Estimation Using Smart Card and Operation Data in Urban Railways.
IEEE Intell. Transp. Syst. Mag., 2023

RouteKG: A knowledge graph-based framework for route prediction on road networks.
CoRR, 2023

STMA-GCN_PedCross: Skeleton Based Spatial-Temporal Graph Convolution Networks with Multiple Attentions for Fast Pedestrian Crossing Intention Prediction.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis.
Proceedings of the 14th International Conference on Ambient Systems, 2023

2022
Deep learning for short-term origin-destination passenger flow prediction under partial observability in urban railway systems.
Neural Comput. Appl., 2022

An Integrated Ride-Matching Model for Shared Mobility on Demand Services.
CoRR, 2022

Dynamic Interlining in Bus Operations.
CoRR, 2022

Real-time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022

2020
Near-on-Demand Mobility. The Benefits of User Flexibility for Ride-Pooling Services.
CoRR, 2020

Assignment-based Path Choice Estimation for Metro Systems Using Smart Card Data.
CoRR, 2020

Short-Term Passenger Flow Prediction With Decomposition in Urban Railway Systems.
IEEE Access, 2020

Multi-Agent Reinforcement Learning for Traffic Signal Control: Algorithms and Robustness Analysis.
Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Revealing Mobility Regularities in Urban Rail Systems.
Proceedings of the 11th International Conference on Ambient Systems, 2020


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